Any person that has walked through NYC streets during winter knows the risks of stepping into an ice covered pothole that will ruin his or her day in an instant. Car drivers also suffer from bad road conditions, and the city is responsible for keeping the streets in good condition. Our story is motivated by an article from the New York Times:
Report Reveals New York City Paid $138 Million in Settlements Related to Potholes on Roadways.
— The New York Times
Such article made us think that street defects are a relevant problem to our daily life–they may cause flat tires, twist (and freeze if its winter) ankles and cost significant money to the city in damage repairs. Here are two examples of street defects:
Pothole Cave-in
Most complaints that are related to street conditions are processed by DOT–Department of Transportation. Hence, we decided to analyze their data from 2015. Our project is structured in the following way:
After subset the related information from 311 Dataset, we picked top eight descriptors we believed are related to street conditions within our new DOT dataset and make the density plot.
According to this plot, Pothole and Street light out seems to be the most common complaints in NYC.
The first step of our analysis consisted on identifying the relevance of stred defects. In general, street defects have a wide range of negative consequences, mostly provoking both pedestrian and vehicle accidents. We decided to focus our study in looking at the relationship between “DOT-complaints about street defects” and “Motor vehicle collisions” in New York City during 2015. We introduced two dataset: “DOT_Complaints” which we subselected from the 311 service dataset. And “NYPD-Motor-Vehicle-collsions” which we found from NYC open data. When processing the datasets, we found out that among “contributes of the accidents” field in the second dataset, some of them are clearly not related to street conditions (for example: “Alcohol Involvement” and “Vehicle Defective”). In order to make our study more accurate, we picked the main causes (Obstruction/Debris’, ‘Other Lighting Defects’, ‘Pavement Defective’ and ‘Pavement Slippery’) that we consider to be related with street defects.
To begin with, we made a gif map to show distribution of the complaints and accidents that happened in NYC during 2015. In this plot, and let each frame represent one month. The colored background represents the areas with most to least DOT complaints (from red to yellow respectively). On top of it, we plotted blue density lines to show the distribution of accidents.
Accidents and street defects:
As we can see, the distribution of the two events keep changing from time to time, but they seem to be centered around the same areas. From here, we break down our study of accidents into two dimensions: Spatial and temporal.
We start our analysis with a time series plot (by day). Here, we used dashed lines to represent complaints and solid lines represent accidents. We also chose a scaled count instead of an actual count to help plotting gvien that there are more complaints than accidents every day.
From the plot, we can see that both events have a peek in mid March, which could suggest that they are also related in time. Although, the increase in complaints actually happened after the increase of accidents, which could be explained by the fact that people probably tend to complain about street defects only after they had an accident.
In this section, we want to explore the causes of street defects. We especially concentrate our attention in three aspects: amount of snow fall, presence of heavy vehicles, and estimated amount of traffic.
In addition to forming ice layers that cause vehicles to skid out of control and have more accidents on average, snow and freezing temperatures harm roads in the following way: When freezing temperatures occur, the water that had previously entered the ground freezes, causing an expansion that breaks the asphalt. Therefore, we expect road defects and snowfall to have a similar trend. In order to find out the relationship between snowfall and street defects, we used the 2010-2015 annual snowfall data from Central Park meteorological observation station.
In the graph above, we have plotted the annual snowfall, road related 311 complaints, and street light related 311 complaints in new york city from 2010 to 2015. The Green line shows the snowfall reached its highest point in 2011, then it decreased in 2012, but increased again in 2013 and 2014. The red line representing road related complaints trend, reveals strong positive correlation with snowfall, which is consistent with our expectation. It also reached its peak in 2011, dropped sharply in 2012, then increased dramatically in 2013 and 2014. Street light related claicomplaints do not show significant fluctuations from 2010 to 2015, which suggests that these are not related with the weather.
As defined in New York City Traffic Rules, a truck is “any vehicle designed for the transportation of property that has the characteristics: two axles and six tires, or three or more axles”. As research reveals, heavy vehicles (including trucks) contribute importantly to roads and bridges damages such as potholes. For purpose of maintaining good road condition, trucks are only allowed traveling on certain roads in New York City. In this section, we used NYC Truck Routes Data from New York City Department of Transportation.